This study presents an innovative approach to the application of large
language models (LLMs) in clinical decision-making, focusing on OpenAI's
ChatGPT. Our approach introduces the use of contextual prompts-strategically
designed to include task description, feature description, and crucially,
integration of domain knowledge-for high-quality binary classification tasks
even in data-scarce scenarios. The novelty of our work lies in the utilization
of domain knowledge, obtained from high-performing interpretable ML models, and
its seamless incorporation into prompt design. By viewing these ML models as
medical experts, we extract key insights on feature importance to aid in
decision-making processes. This interplay of domain knowledge and AI holds
significant promise in creating a more insightful diagnostic tool.
Additionally, our research explores the dynamics of zero-shot and few-shot
prompt learning based on LLMs. By comparing the performance of OpenAI's ChatGPT
with traditional supervised ML models in different data conditions, we aim to
provide insights into the effectiveness of prompt engineering strategies under
varied data availability. In essence, this paper bridges the gap between AI and
healthcare, proposing a novel methodology for LLMs application in clinical
decision support systems. It highlights the transformative potential of
effective prompt design, domain knowledge integration, and flexible learning
approaches in enhancing automated decision-making.

该研究提出了一种创新的方法，在临床决策中应用大型语言模型（LLMs），重点关注 OpenAI 的 ChatGPT。我们的方法介绍了在数据稀缺情况下，采用上下文提示（策略性地设计了包括任务描述、特征描述和领域知识整合的提示）进行高质量二元分类任务的应用。我们的研究探索了 LLMs 基于零样本和少样本提示学习的动态性，通过比较 OpenAI 的 ChatGPT 在不同数据条件下与传统监督式机器学习模型的性能，旨在提供对不同数据可用性下提示工程策略有效性的见解。这篇论文架起了人工智能和医疗保健之间的桥梁，提出了一种新的 LLMs 在临床决策辅助系统中的应用方法，并强调了提示设计、领域知识整合和灵活学习方法在增强自动化决策方面的变革潜力。